Unleash the Potential of CLIP for Video Highlight Detection
Donghoon Han, Seunghyeon Seo, Eunhwan Park, Seong-Uk Nam, Nojun Kwak
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ReproduceCode
- github.com/dhk1349/HL-CLIPpytorch★ 6
Abstract
Multimodal and large language models (LLMs) have revolutionized the utilization of open-world knowledge, unlocking novel potentials across various tasks and applications. Among these domains, the video domain has notably benefited from their capabilities. In this paper, we present Highlight-CLIP (HL-CLIP), a method designed to excel in the video highlight detection task by leveraging the pre-trained knowledge embedded in multimodal models. By simply fine-tuning the multimodal encoder in combination with our innovative saliency pooling technique, we have achieved the state-of-the-art performance in the highlight detection task, the QVHighlight Benchmark, to the best of our knowledge.
Tasks
Benchmark Results
| Dataset | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| QVHighlights | HL-CLIP | mAP | 41.94 | — | Unverified |